Skip to content
Predict the viability of organ transplants using deep learning
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Type Name Latest commit message Commit time
Failed to load latest commit information.

Deep Learning Report (

Devpost (


Everyday twenty-two people die waiting for a transplant. Around 65% of organs are rejected by centers while 10% of accepted organs are not used in transplants. Rejections occur due to low confidence from doctors about compatibility, time spent waiting for decisions being made, and human error. This $34B industry is missing many chances at saving lives and money. UNOS stores data for the thousands of transplants that occur and current medical charts have hundreds of data points on patients. This seemed like a perfect opportunity to let machines solve this problem.

What it does

Our system takes in donor and recipient medical data and predicts the viability of a transplant succeeding. The model was trained with synthetic data based on worldwide organ transplant trends. We have a system for both UNOS (and other administrators) and doctors to predict the chances of success (using many data points such as antibodies, blood type, age, ethnicity, distance, etc.)

How we built it

We used keras to iterate and improve our deep learning model quickly. To generate the synthetic data we used python. The frontend was built using angular and react.

Challenges we ran into

Our product is centered around historic data related to organ transplants, and the biggest challenge that we faced was gaining access to this information. Since this is a highly regulated field, the government and organizations involved in organ transplants tend to keep this information highly confidential. However, in order to replicate original trends, we created synthetic data based on factual information, and used these probabilities to populate our data set.

Accomplishments that we're proud of

Working together to solve real world problems.

You can’t perform that action at this time.